An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

Due to the influence of unpredictable random events, the processing time of each operation should be treated as random variables if we aim at a robust production schedule. However, compared with the extensive research on the deterministic model, the stochastic job shop scheduling problem (SJSSP) has...

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Main Authors: Rui Zhang, Cheng Wu
Format: Article
Language:English
Published: MDPI AG 2011-09-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/13/9/1708/
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author Rui Zhang
Cheng Wu
author_facet Rui Zhang
Cheng Wu
author_sort Rui Zhang
collection DOAJ
description Due to the influence of unpredictable random events, the processing time of each operation should be treated as random variables if we aim at a robust production schedule. However, compared with the extensive research on the deterministic model, the stochastic job shop scheduling problem (SJSSP) has not received sufficient attention. In this paper, we propose an artificial bee colony (ABC) algorithm for SJSSP with the objective of minimizing the maximum lateness (which is an index of service quality). First, we propose a performance estimate for preliminary screening of the candidate solutions. Then, the K-armed bandit model is utilized for reducing the computational burden in the exact evaluation (through Monte Carlo simulation) process. Finally, the computational results on different-scale test problems validate the effectiveness and efficiency of the proposed approach.
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spelling doaj.art-1344d1bbc95f41ff86421d09731c747d2022-12-22T04:04:14ZengMDPI AGEntropy1099-43002011-09-011391708172910.3390/e13091708An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing TimesRui ZhangCheng WuDue to the influence of unpredictable random events, the processing time of each operation should be treated as random variables if we aim at a robust production schedule. However, compared with the extensive research on the deterministic model, the stochastic job shop scheduling problem (SJSSP) has not received sufficient attention. In this paper, we propose an artificial bee colony (ABC) algorithm for SJSSP with the objective of minimizing the maximum lateness (which is an index of service quality). First, we propose a performance estimate for preliminary screening of the candidate solutions. Then, the K-armed bandit model is utilized for reducing the computational burden in the exact evaluation (through Monte Carlo simulation) process. Finally, the computational results on different-scale test problems validate the effectiveness and efficiency of the proposed approach.http://www.mdpi.com/1099-4300/13/9/1708/shop schedulingartificial bee colony algorithmmaximum latenesssimulation
spellingShingle Rui Zhang
Cheng Wu
An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times
Entropy
shop scheduling
artificial bee colony algorithm
maximum lateness
simulation
title An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times
title_full An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times
title_fullStr An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times
title_full_unstemmed An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times
title_short An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times
title_sort artificial bee colony algorithm for the job shop scheduling problem with random processing times
topic shop scheduling
artificial bee colony algorithm
maximum lateness
simulation
url http://www.mdpi.com/1099-4300/13/9/1708/
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